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Research Article Submitted to Open Behavioral Genetics on ?/07/2014 Published in Open Behavioral Genetics on Sexual selection as an evolutionary mechanism behind sex and population differences in intelligence Davide Piffer1 Abstract Sexual dimorpshism in intelligence suggests that this phenotype is a sexually selected trait. Sexual selection has the double effect of increasing average population phenotype and reducing genetic variation in sexually selected traits. Matching these predictions, the average country IQ (estimated from PISA Creative Problem Solving) is positively correlated to sex dimorphism and the latter in turn is inversely correlated to variance in intelligence scores within populations. Average country male height is negatively correlated to sex dimorphism in intelligence, supporting the notion of a trade-off between selection for brain and brawn. Introduction Sexual selection is responsible for sexual dimorphism across species and a variety of traits (Lande, 1980). However, sexual selection raises the average phenotypic trait value not only in the selected sex, but to a lesser extent also in the opposite sex, via the mechanism of genetic correlation between homologous characters of the sexes (that is, the correlation between the additive effects of genes as expressed in males and females) (Lande, 1980). Sexual dimorphism can thus be regarded as an indicator of the strenght of sexual selection on a given phenotype. If intelligence is a sexually selected trait in human populations, there should be sexual dimorphism on IQ scores. There is evidence that males outperform females, after the end of puberty ((Lynn (1999), Colom and Lynn (2004), Lynn and Irwing (2004), also see table 1)). 1 [email protected] 1 There is genomic evidence lending support to the idea that intelligence is a sexually selected trait. Fisher (1931) proposed that genes having differential fitness effects on males and females should be found on the sex chromosomes. Hurst (2001) proposed that, relative to autosomal loci, a X-linked locus is much more likely to be responsible for sexual development, especially if that locus is advantageous to males. If a locus is male-advantageous, its expression will be enhanced in males but suppressed in females (if it is disadvantageous to them). It could be reasonably expected that genes advantageous to males are on the Y-chromosomes, as this would guarantee that they are expressed only in males. However, many genes involved in spermatogenesis in mice are X-linked and expressed (exclusively) in males (Wang et al. 2001) and in humans, the X chromosome is enriched for male-specific but not female-specific genes (Lercher et al., 2003). Indeed, the X chromosome was found to host a substantial portion (one third) of genetic variation for sexually selected traits in a meta-analysis of reciprocral crosses from a variety of mammal and insects(Reinhold, 1998) and it has probably been engaged in the development of sexually selected characteristics for at least 300 million years (Zechner et al., 2001). In particular, the X chromosome seems to be disproportionately involved in cases of Mendelian inheritance of mental retardation (Skuse, 2005) and there seems to be a concentration of intellectual disability genes on this chromosome that is not due to ascertainment bias (Zechner et al., 2001; Gécz, 2004; Ropers and Hamel, 2005; Delbridge et al., 2008). Crespi et al. (2010) found that 69.7% of X-linked intellectual disability genes show primary central nervous system function, compared to 49.2% of autosomal ones. For this reason, human males are “more likely than females to be influenced by haplotypes that are associated with exceptionally high abilities (…), they are also more likely to show deficits in mental abilities than females because of the impact of deleterious mutations carried in haploid state (Skuse, 2005). Dosage differences in the expression of X-linked genes is a possible mechanism that accounts for male-female neural differentiation (Skuse, 2005). Thus, sexual selection operated on males via intersexual competition (female choice or preference for smarter men) or intrasexual competition (competition between males for access to females), possibly via the benefits accrued by higher social status or wealth acquired by more intelligent men. Moreover, men historically have performed endeavours such as hunting and making war to other tribes or nations, which require greater fluid intelligence and strategic planning and the best warriors and hunters have traditionally enjoyed a dramatic boost in status, which would have translated in better reproductive success. Since sex dimorphism is an indicator of sexual selection, the degree of sex dimorphism indicates the strength of selection. A prediction of the hypothesis that intelligence is a sexually selected trait is that the average intelligence of populations is positively correlated to sex dimorphism.The extent of sexual dimorphism is assumed to indicate the strenght of selection for intelligence, because the correlation between the homologous characters of the sexes increases the average phenotype of both males and females to different extents (Lande, 1980). Indeed, “when the sexes vary equally and are under equally strong natural selection towards different optima, and constant intensities of sexual selection, the average phenotype of the two sexes together evolves on a fast time scale, while the sexual dimorphism (the difference in the 2 mean phenotypes of the two sexes) evolves on a slow time scale” (Lande, 1980). This is because the amount of genetic variation whose phenotypic expression is sex-limited is usually much smaller than the autosomal genetic variation (Lande, 1980). Accordingly, Fitzpatrick (2004) found that the majority of putatively sexually selected genes are pleiotropic and not preferentially sex linked. Another effect of sexual selection is a reduction in genetic variation for the trait in the sex on which sexual selection acts (Van Homrigh et al.,2007; Tomkins et al, 2004), due to favoured alleles becoming rapidly fixed.Thus, the phenotypic variance should be lower in populations with stronger sexual selection, predicting an inverse correlation between sex difference in IQ and standard deviation in IQ across populations. Another prediction, stemming from the hypothesis that intelligence genes are sex-linked (thus potentially the target of sexual selection) is that male SD is higher than female SD. Piffer (2014) found evidence for an inverse correlation between frequencies of height and IQincreasing alleles between populations, which he interpreted as the result of opposite selective pressure on these two phenotypes. A possible mechanism to account for this finding is sexual selection, if a trade-off exists between physical and intellectual competition, implying that intelligence and physical strength are opposite or conflicting strategies employed by males for attracting or controlling females. To test this “brawn vs brain” evolutionary model, data on average height were employed. A prediction of this evolutionary model is that populations with higher average intelligence and sex dimorphism in intelligence will have lower average height (and lower sexual dimorphism in height). Methods Scores on a test of fluid intelligence (PISA Creative Problem Solving) were used as measures of country level intelligence (OECD, 2014). IQ and PISA will be used interchangeably throughout the paper. The OECD (2014) has recently published data for the 2012 results of the performance of 15 year students in the PISA Creative Problem Solving (CPS), a measure of students’ ability to solve problems in “non-routine situations” defined as “situations that require at least 30 minutes to find a good solution” (p.26). The solution of these problems requires the ability “to think flexibly and creatively about how to overcome the barriers that stand in the way of a solution” (p.26). A “ready-made strategy” or a mastery of facts and procedures is not sufficient for the solution of these problems. The creative problem solving assessment assesses “students’ general reasoning skills, their ability to regulate problem-solving processes, and their willingness to do so, by confronting students with problems that do not require expert knowledge to solve”. The test measures the ability to solve problems in “situations that students may encounter outside of school as part of their everyday experience” (e.g. technology devices, unfamiliar spaces, food or drink) (p.31) and “an individual’s capacity to engage in cognitive processing to understand and resolve problem situations where a method of solution is not immediately obvious” (p.32) involving "scenarios related to real life problems" in four the areas of technology, non-technology, personal and social. Thus, the terms PISA CPS scores and IQ 3 will be used interchangeably throughout this paper. In terms of Cattell’s (1971) concepts of fluid and crystallized intelligence, the Creative Problem Solving test is a measure of fluid intelligence defined as the ability to think logically and solve problems in novel situations, independent of acquired knowledge. Scores on a test of fluid intelligence (PISA Creative Problem Solving) were used as measures of country level intelligence (OECD, 2014). IQ and PISA will be used interchangeably throughout the paper. Average scores along with standard deviation for the total sample and separately for males and females are reported in table 1. Mean population height was retrieved from Wikipedia (Human Height). All of the chosen studies provided measured height, were published after 2000, and were performed on young subjects (17-39 years). Only male height was used because female height was not available for many countries. Gross Domestic Product at purchasing power per capita (GDP (PPP)) was used as an independent variable due to its potential relationship with IQ and sex differences in country scores. That is, GDP is known to be positively related to country IQ (Lynn and Vanhanen, 2006, 2012, Rindermann (2012), Sailer (2012)) and could predict sex difference in IQ, possibly with more economically developed countries showing lower sex difference (lower male advantage). Results The average country PISA score was positively correlated with sex difference (r= 0.225) albeit not significantly (p= 0.142; N=44). However, the partial correlation between sex difference and average country score (controlling for GDP) was significant (r=0.344; p= 0.024; N=44). Confirming expectations, GDP was negatively correlated with sex difference in IQ (r= -.169) albeit not significantly (p= 0.273; N=44) and it was positively correlated to IQ (r= .454; p= 0.02; N=44). Sex difference in IQ was negatively correlated with the SD (r=-.465; p= 0.01; N=44). After removing the effect of GDP, this correlation was slightly stronger (r= - .484; p= 0.01; N= 41). Table 1. OECD Australia PISA Score Total PISA Males 523 524 PISA Females 522 Standard Deviation SD Difference Total Boys 2 97 100 SD Girls 95 GDP 43550 4 Austria 506 512 500 12 94 98 90 44168 Belgium 508 512 504 8 106 110 102 40338 Canada 526 528 523 5 100 104 96 43207 Chile 448 455 441 14 86 89 82 21911 Czech Republic 509 513 505 8 95 98 92 27334 Denmark 497 502 492 10 92 94 90 42790 Estonia 515 517 513 4 88 91 84 25049 Finland 523 520 526 -6 93 96 89 38251 France 511 513 509 4 96 100 93 36907 Germany 509 512 505 7 99 103 94 43332 Hungary 459 461 457 4 104 110 99 22190 Ireland 498 501 496 5 93 97 89 43304 Israel 454 457 451 6 123 134 112 32760 Italy 510 518 500 18 91 97 82 34303 Japan 552 561 542 19 85 89 79 36315 Korea 561 567 554 13 91 95 87 33140 Netherlands 511 513 508 5 99 101 96 43404 Norway 503 502 505 -3 103 106 99 65461 Poland 481 481 481 0 96 103 90 23275 Portugal 494 502 486 16 88 91 84 25892 Slovak Republic 483 494 472 22 98 100 94 25333 Slovenia 476 474 478 -4 97 102 91 27915 Spain 477 478 476 2 107 109 99 32103 Sweden 491 489 493 -4 96 101 91 43455 Turkey 454 462 447 15 79 81 77 18975 England 517 520 514 6 97 98 95 35209 United States 508 509 506 3 93 97 89 53143 Brazil 428 440 418 22 92 95 87 15034 Bulgaria 402 394 410 -16 107 110 102 15941 Colombia 399 415 385 30 92 92 89 12371 Croatia 466 474 459 15 92 98 85 20904 Cyprus 445 440 449 -9 99 107 90 30489 Hong Kong 540 546 532 14 92 93 90 53203 Non-Oecd 5 Macao-China 540 546 535 11 79 81 77 142564 Malaysia 422 427 419 8 84 86 81 23298 Montenegro 407 404 409 -5 92 95 88 14318 Russian Fed 489 493 485 8 88 89 87 24120 Serbia 473 481 466 15 89 90 88 12374 Shangai-China 536 549 524 25 90 90 88 11904 Singapore 562 567 558 9 95 100 89 78744 Chinese Taipei 534 540 528 12 91 96 85 11904 United Arab Emirates 411 398 424 -26 106 114 95 53780 Uruguay 403 409 398 11 97 102 93 19590 Table 2. Correlational matrix. AverageScore Sexdifference SDTotal GDPpc MaleHeight AverageScore r p N Sexdifference r p N SDTotal r p N GDPpc r p N MaleHeight r p N 0.225 0.142 44 -.207 .179 44 -.465** .001 44 .454** .001 44 -.169 .273 44 -.064 .681 44 -.136 .422 37 -.426** .008 37 .238 .156 37 .064 .708 37 A multiple linear regression was run with PISA CPS score as dependent variable, and sex difference, GDP as independent variables. Using the enter method, a significant model emerged (F2,41= 8.774). R= .547; R²= .300; Adjusted R²= .266. Outcome Predictor Beta p 6 PISA CPS Score Sex Difference .311 .024 GDP PPP .506 .000 Another regression was carried out with SD in PISA scores as dependent variable and sex difference, GDP, PISA score as independent variables. Using the enter method, a significant model emerged (F2,41= 4.175). R= .488; R²= .238; Adjusted R²= .200. Outcome Predictor Beta p Sex Difference -.477 .003 GDP PPP -.125 .448 PISA score -.043 .798 PISA SD Brawn vs. Brain Confirming expectations, average male height was found to be (non significantly) negatively related to PISA CPS score (r= -.136; p=.422; N=37), and (significantly) to sex dimorphism in intelligence (r= -.426; p=.008; N=37). Discussion The predictions generated by the hypothesis that intelligence has undergone sexual selection in males are supported by the results. The average intelligence of populations was found to be positively correlated to sex dimorphism in intelligence scores, matching the prediction that sexual selection has the double effect of increasing average phenotypic level and creating sex differences in the selected trait. The correlation reached significance after removing the effect of GDP, suggesting that economic development masks the relationship between sex dimorphism in IQ and country IQ scores through its negative effect on male intellectual advantage and its positive association with country IQ. Another effect of sexual selection is to reduce the amount of genetic variation in sexually selected traits. I found that populations with higher sex dimorphism in intelligence also had lower variance in intelligence scores, thus matching the second prediction of the sexual selection model. A note of caution regarding this finding is necessary, as phenotypic variation is 7 correlated to genetic variation but environmental noise due to population stratification (SES, ethnicity, etc.) can easily attenuate the genetic signal. Supporting the brawn vs brain evolutionary scenario, male height was found to be negatively related to sex differences in intelligence, which is a proxy for sexual selection strength. This suggests that there is a trade-off in sexual selection between physical power or attractiveness and intellectual abilities. This provides a possible explanation for the finding by Piffer (2014) that frequencies of alleles known to increase height had a strong inverse correlation between populations to frequencies of alleles that increase IQ The results of this study have multiple implications. First, the evolution of intelligence has been probably affected by evolutionary forces that acted differently on males and females. Although with the present data it is impossible to determine the precise mechanism (i.e. whether it is was due to female choice or higher reproductive success of high IQ males via higher wealth and social status) this study provides encouraging results for future investigations into the role played by sexual selection on intelligence during prehistoric and historic times. Another implication of this study is that intelligence has continued to evolve after different human populations migrated out of Africa and possibly up to the 19th century, as suggested by the substantial variability in sex differences even between neighbouring countries. Finally, the failure of GWAS to find genes accounting for a significant variation in intelligence could be due to their exclusive focus on the autosomal genome and the findings presented in this paper could provide a rationale for an extension of genomic studies of cognition to the sex chromosomes References: Cattell, R.B. (1971). Abilities: Their Structure, Growth and Action. Boston: Houghton Mifflin. Colom, R., & Lynn, R. (2004). Testing the developmental theory of sex differences in intelligence on 12–18 year olds. Personality and Individual Differences, 36(1), 75-82. Crespi, B., Summers, K., Dorus, S. (2010). Evolutionary Genomics of Human Intellectual Disability. Evolutionary Applications, 3: 52-63. 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